A study of order based genetic and evolutionary algorithms in combinatorial optimization problems

In Genetic and Evolutionary Algorithms (GEAs) one is faced with a given number of parameters, whose possible values are coded in a binary alphabet. With Order Based Representations (OBRs) the genetic information is kept by the order of the genes and not by its value. The application of OBRs to the Traveling Salesman Problem (TSP) is a well known technique to the GEA community. In this work one intends to show that this coding scheme can be used as an indirect representation, where the chromosome is the input for the decoder. The behavior of the GEA's operators is compared under benchmarks taken from the Combinatorial Optimization arena.(undefined

Real-Time Forecasting by Bio-Inspired Models

In recent years, bio-inspired methods for problem solving, such as Artificial Neural Networks (ANNs) or Genetic and Evolutionary Algorithms (GEAs), have gained an increasing acceptance as alternative approaches for forecasting, due to advantages such as nonlinear learning and adaptive search. The present work reports the use of these techniques for Real-Time Forecasting (RTF), where there is a need for an autonomous system capable of fast replies. Comparisons among bio-inspired and conventional approaches (e.g., Exponential Smoothing), revealed better forecasting performances for the evolutionary and connectionist models.

Sub-structural Niching in Estimation of Distribution Algorithms

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS